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Course Outline
Introduction to Generative AI and Agentic AI
- Defining Generative AI and Agentic AI
- Key differences and synergies between the two
- Industry use cases and current trends
Generative AI Architecture and Tools
- Transformer-based models: GPT, LLaMA, Claude, and others
- Comparing fine-tuning with in-context learning
- Essential tools: ChatGPT, Hugging Face Transformers, Google AI Studio
Prompt Engineering for Control and Structure
- Prompt patterns for writing, coding, summarization, and more
- Techniques such as few-shot, zero-shot, and chain-of-thought prompting
- Leveraging prompt libraries and testing utilities
Understanding Agentic AI
- Definition and evolution of agentic AI
- Core architectures: planning, memory, tool usage, and self-reflection
- Leading frameworks: AutoGPT, BabyAGI, CrewAI, LangGraph
Designing and Deploying Autonomous Agents
- Establishing goals and decomposing tasks
- Integrating tools and APIs (search, memory, code execution)
- Coordinating multi-agent systems and incorporating human-in-the-loop supervision
Use Cases and Implementation Scenarios
- Distinguishing between content generation and task orchestration
- Applications in enterprise productivity, customer support, and data extraction
- Ensuring responsible and secure implementation practices
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning principles
- Experience using APIs or scripting languages such as Python
- Familiarity with prompt engineering or the utilization of large language models
Target Audience
- AI developers and engineers
- Innovation and Research & Development (R&D) teams
- Technical product managers interested in exploring agentic AI systems
14 Hours
Testimonials (1)
the tips and recommended prompts that we can take away from this training
